Eksplorasi Data Retail

Membuat Data Frame

df <- data.frame(store=c("Jakarta","Bandung","Surabaya"), sales=c(1000,2000,3000), revenue=c(150,210,500))
df

class(df)

## [1] "data.frame"

Eksplorasi Data Frame Retail

Membaca data retail dan assign dengan nama retail

getwd()

## [1] "E:/workspace/TRAINING ALGORITMA/DAY 1/online/P4DS"

# cara 1 -> relative path
retail <- read.csv(file = "data_input/retail.csv", sep = ",")
retail

# cara 2 -> full path
retail2 <- read.csv(file = "E:/workspace/TRAINING ALGORITMA/DAY 1/online/P4DS/data_input/retail.csv", sep = ",")
retail2

Inspect our data menggunakan names(), length(), dan dim()

names(retail) #info nama kolom

##  [1] "Row.ID"       "Order.ID"     "Order.Date"   "Ship.Date"    "Ship.Mode"   
##  [6] "Customer.ID"  "Segment"      "Product.ID"   "Category"     "Sub.Category"
## [11] "Product.Name" "Sales"        "Quantity"     "Discount"     "Profit"

length(retail) # jumlah kolom

## [1] 15

dim(retail) # dimensi

## [1] 9994   15

Investigasi data dengan head() dan tail()

head(x = retail, n = 10) #top n

tail(x = retail, n = 2) #least n

Melihat struktur data dengan fungsi str()

str(retail)

## 'data.frame':    9994 obs. of  15 variables:
##  $ Row.ID      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Order.ID    : chr  "CA-2016-152156" "CA-2016-152156" "CA-2016-138688" "US-2015-108966" ...
##  $ Order.Date  : chr  "11/8/16" "11/8/16" "6/12/16" "10/11/15" ...
##  $ Ship.Date   : chr  "11/11/16" "11/11/16" "6/16/16" "10/18/15" ...
##  $ Ship.Mode   : chr  "Second Class" "Second Class" "Second Class" "Standard Class" ...
##  $ Customer.ID : chr  "CG-12520" "CG-12520" "DV-13045" "SO-20335" ...
##  $ Segment     : chr  "Consumer" "Consumer" "Corporate" "Consumer" ...
##  $ Product.ID  : chr  "FUR-BO-10001798" "FUR-CH-10000454" "OFF-LA-10000240" "FUR-TA-10000577" ...
##  $ Category    : chr  "Furniture" "Furniture" "Office Supplies" "Furniture" ...
##  $ Sub.Category: chr  "Bookcases" "Chairs" "Labels" "Tables" ...
##  $ Product.Name: chr  "Bush Somerset Collection Bookcase" "Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back" "Self-Adhesive Address Labels for Typewriters by Universal" "Bretford CR4500 Series Slim Rectangular Table" ...
##  $ Sales       : num  262 731.9 14.6 957.6 22.4 ...
##  $ Quantity    : int  2 3 2 5 2 7 4 6 3 5 ...
##  $ Discount    : num  0 0 0 0.45 0.2 0 0 0.2 0.2 0 ...
##  $ Profit      : num  41.91 219.58 6.87 -383.03 2.52 ...

Kolom apa yang tipenya belum sesuai? - order.date -> date - ship.date -> date - ship.mode -> factor - category -> factor - subcategory -> factor - segment -> factor

Dari struktur data di atas, variabel/kolom Order.Date dan Ship.Date perlu diubah ke tipe data yang sesuai. Ubahlah kedua variabel tersebut ke bentuk yang sesuai

Ubahlah Order.Date dan Ship.Date ke tipe date

retail$Order.Date<-as.Date(retail$Order.Date, format = "%m/%d/%y")
retail

Ubahlah kolom Ship.Date ke dalam tipe date

retail$Ship.Date<-as.Date(retail$Ship.Date, format = "%m/%d/%y")
retail

Ubahlah variabel kolom Ship.Mode dan Segment ke tipe factor

retail$Ship.Mode <- as.factor(retail$Ship.Mode)
retail$Segment <- as.factor(retail$Segment)
retail

Subsetting

Menduplikasi fungsi head dengan subsetting retail [ , ]

objek[baris, kolom]

head(retail)

retail[1:6, ] #[<baris>,<kolom>]

Subsetlah data ke 1, 5,7,10 untuk kolom Ship.Mode dan Profit

#cara 1
retail[c(1,5,7,10),c(1,5)]

#cara 2
retail[c(5:10,25:30),c("Ship.Mode","Profit")]

Buanglah variabel Row.ID, Order.ID, Customer.ID, kemudian assign ke object bernama retail_clean. Menggunakan data[,-c()] untuk menghapus kolom tertentu.

#cara 1
retail_clean <- retail[,-c(1,2,6)]
retail_clean

#cara 2
retail_clean2<-subset(x=retail, select = -c(1,2,6))
retail_clean2

Melihat rangkuman dari data retail_clean menggunakan fungsi summary()

retail_clean$Category<-as.factor(retail_clean$Category)
retail_clean$Sub.Category<-as.factor(retail_clean$Sub.Category)
retail_clean$Product.Name<-as.factor(retail_clean$Product.Name)
summary(retail_clean)

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-03   Min.   :2014-01-07   First Class   :1538  
##  1st Qu.:2015-05-23   1st Qu.:2015-05-27   Same Day      : 543  
##  Median :2016-06-26   Median :2016-06-29   Second Class  :1945  
##  Mean   :2016-04-30   Mean   :2016-05-03   Standard Class:5968  
##  3rd Qu.:2017-05-14   3rd Qu.:2017-05-18                        
##  Max.   :2017-12-30   Max.   :2018-01-05                        
##                                                                 
##         Segment      Product.ID                   Category   
##  Consumer   :5191   Length:9994        Furniture      :2121  
##  Corporate  :3020   Class :character   Office Supplies:6026  
##  Home Office:1783   Mode  :character   Technology     :1847  
##                                                              
##                                                              
##                                                              
##                                                              
##       Sub.Category                      Product.Name      Sales          
##  Binders    :1523   Staple envelope           :  48   Min.   :    0.444  
##  Paper      :1370   Easy-staple paper         :  46   1st Qu.:   17.280  
##  Furnishings: 957   Staples                   :  46   Median :   54.490  
##  Phones     : 889   Avery Non-Stick Binders   :  20   Mean   :  229.858  
##  Storage    : 846   Staples in misc. colors   :  19   3rd Qu.:  209.940  
##  Art        : 796   KI Adjustable-Height Table:  18   Max.   :22638.480  
##  (Other)    :3613   (Other)                   :9797                      
##     Quantity        Discount          Profit         
##  Min.   : 1.00   Min.   :0.0000   Min.   :-6599.978  
##  1st Qu.: 2.00   1st Qu.:0.0000   1st Qu.:    1.729  
##  Median : 3.00   Median :0.2000   Median :    8.666  
##  Mean   : 3.79   Mean   :0.1562   Mean   :   28.657  
##  3rd Qu.: 5.00   3rd Qu.:0.2000   3rd Qu.:   29.364  
##  Max.   :14.00   Max.   :0.8000   Max.   : 8399.976  
## 

Dari summary di atas, apa informasi yang kita dapatkan?

1. rata rata diskon 15,62%
2. segmen terbesar ada di consumer
3. perngiriman terbanyak pake standart
4. profit ada kerugian

Dive Deeper

1. Periksalah summary dari data-data yang rugi

summary(retail_clean[retail_clean$Profit<=0,] )

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-04   Min.   :2014-01-08   First Class   : 303  
##  1st Qu.:2015-05-06   1st Qu.:2015-05-09   Same Day      :  99  
##  Median :2016-06-12   Median :2016-06-16   Second Class  : 315  
##  Mean   :2016-04-25   Mean   :2016-04-29   Standard Class:1219  
##  3rd Qu.:2017-05-05   3rd Qu.:2017-05-09                        
##  Max.   :2017-12-30   Max.   :2018-01-03                        
##                                                                 
##         Segment      Product.ID                   Category        Sub.Category
##  Consumer   :1038   Length:1936        Furniture      :747   Binders    :613  
##  Corporate  : 573   Class :character   Office Supplies:915   Chairs     :255  
##  Home Office: 325   Mode  :character   Technology     :274   Tables     :205  
##                                                              Storage    :185  
##                                                              Furnishings:176  
##                                                              Phones     :138  
##                                                              (Other)    :364  
##                                           Product.Name      Sales          
##  Global Wood Trimmed Manager's Task Chair, Khaki:  12   Min.   :    0.444  
##  Global High-Back Leather Tilter, Burgundy      :  10   1st Qu.:   12.859  
##  KI Adjustable-Height Table                     :  10   Median :   73.170  
##  SAFCO Commercial Wire Shelving, Black          :  10   Mean   :  256.402  
##  Hon 61000 Series Interactive Training Tables   :   9   3rd Qu.:  298.581  
##  GBC Instant Report Kit                         :   8   Max.   :22638.480  
##  (Other)                                        :1877                      
##     Quantity         Discount         Profit         
##  Min.   : 1.000   Min.   :0.000   Min.   :-6599.978  
##  1st Qu.: 2.000   1st Qu.:0.200   1st Qu.:  -55.490  
##  Median : 3.000   Median :0.400   Median :  -16.920  
##  Mean   : 3.768   Mean   :0.469   Mean   :  -80.646  
##  3rd Qu.: 5.000   3rd Qu.:0.700   3rd Qu.:   -5.631  
##  Max.   :14.000   Max.   :0.800   Max.   :    0.000  
## 

2. Periksalah summary dari data-data yang untung

summary(retail_clean[retail_clean$Profit>0,] )

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-03   Min.   :2014-01-07   First Class   :1235  
##  1st Qu.:2015-05-26   1st Qu.:2015-05-29   Same Day      : 444  
##  Median :2016-06-28   Median :2016-07-02   Second Class  :1630  
##  Mean   :2016-05-01   Mean   :2016-05-04   Standard Class:4749  
##  3rd Qu.:2017-05-15   3rd Qu.:2017-05-19                        
##  Max.   :2017-12-30   Max.   :2018-01-05                        
##                                                                 
##         Segment      Product.ID                   Category   
##  Consumer   :4153   Length:8058        Furniture      :1374  
##  Corporate  :2447   Class :character   Office Supplies:5111  
##  Home Office:1458   Mode  :character   Technology     :1573  
##                                                              
##                                                              
##                                                              
##                                                              
##       Sub.Category 
##  Paper      :1370  
##  Binders    : 910  
##  Art        : 796  
##  Furnishings: 781  
##  Phones     : 751  
##  Accessories: 683  
##  (Other)    :2767  
##                                                     Product.Name 
##  Staple envelope                                          :  48  
##  Easy-staple paper                                        :  46  
##  Staples                                                  :  46  
##  Staples in misc. colors                                  :  19  
##  Logitech 910-002974 M325 Wireless Mouse for Web Scrolling:  15  
##  Avery Non-Stick Binders                                  :  14  
##  (Other)                                                  :7870  
##      Sales             Quantity         Discount           Profit        
##  Min.   :    0.99   Min.   : 1.000   Min.   :0.00000   Min.   :   0.063  
##  1st Qu.:   17.94   1st Qu.: 2.000   1st Qu.:0.00000   1st Qu.:   5.325  
##  Median :   50.97   Median : 3.000   Median :0.00000   Median :  13.318  
##  Mean   :  223.48   Mean   : 3.795   Mean   :0.08104   Mean   :  54.918  
##  3rd Qu.:  191.98   3rd Qu.: 5.000   3rd Qu.:0.20000   3rd Qu.:  40.466  
##  Max.   :17499.95   Max.   :14.000   Max.   :0.40000   Max.   :8399.976  
## 

Dari hasil summary tersebut, apa insight yang bisa diambil? 1. Sub Kategory plg merugi adalah binder 2. Paling untung adalah paper 3. chair & table perlu dievaluasi penjualan karena tidak menyumbang profit

Subsetting dengan 2 kondisi

Untuk mensubset dengan 2 kondisi dan kedua kondisinya terpenuhi, bisa menggunakan operator & dan

Misal saya ingin periksa untuk barang2 yang untung (profit > 0) dan juga didiskon (discount > 0), kebanyakan dari cetagory apa

summary(retail_clean[retail_clean$Profit>0 & retail_clean$Discount>0,] )

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-03   Min.   :2014-01-07   First Class   : 540  
##  1st Qu.:2015-05-08   1st Qu.:2015-05-12   Same Day      : 186  
##  Median :2016-07-02   Median :2016-07-03   Second Class  : 633  
##  Mean   :2016-04-29   Mean   :2016-05-03   Standard Class:1931  
##  3rd Qu.:2017-05-26   3rd Qu.:2017-05-28                        
##  Max.   :2017-12-30   Max.   :2018-01-04                        
##                                                                 
##         Segment      Product.ID                   Category   
##  Consumer   :1705   Length:3290        Furniture      : 538  
##  Corporate  :1029   Class :character   Office Supplies:2010  
##  Home Office: 556   Mode  :character   Technology     : 742  
##                                                              
##                                                              
##                                                              
##                                                              
##       Sub.Category 
##  Binders    : 573  
##  Paper      : 513  
##  Phones     : 442  
##  Art        : 298  
##  Chairs     : 229  
##  Accessories: 212  
##  (Other)    :1023  
##                                                      Product.Name 
##  Staples                                                   :  23  
##  Staple envelope                                           :  18  
##  Easy-staple paper                                         :  14  
##  Avery Non-Stick Binders                                   :  11  
##  GBC Premium Transparent Covers with Diagonal Lined Pattern:   9  
##  Newell 312                                                :   9  
##  (Other)                                                   :3206  
##      Sales              Quantity        Discount          Profit        
##  Min.   :    1.344   Min.   : 1.00   Min.   :0.1000   Min.   :   0.153  
##  1st Qu.:   15.802   1st Qu.: 2.00   1st Qu.:0.2000   1st Qu.:   3.757  
##  Median :   47.984   Median : 3.00   Median :0.2000   Median :   9.312  
##  Mean   :  219.140   Mean   : 3.78   Mean   :0.1985   Mean   :  36.942  
##  3rd Qu.:  202.784   3rd Qu.: 5.00   3rd Qu.:0.2000   3rd Qu.:  29.262  
##  Max.   :11199.968   Max.   :14.00   Max.   :0.4000   Max.   :3919.989  
## 

Untu mensubset dengan 2 kondisi tetapi setidaknya salah satu kondisinya terpenuhi, bisa menggunakan operator | atau

summary(retail_clean[retail_clean$Category=="Office Supplies" | retail_clean$Segment=="Consumer",] )

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-03   Min.   :2014-01-07   First Class   :1223  
##  1st Qu.:2015-05-12   1st Qu.:2015-05-16   Same Day      : 450  
##  Median :2016-06-18   Median :2016-06-21   Second Class  :1569  
##  Mean   :2016-04-25   Mean   :2016-04-29   Standard Class:4848  
##  3rd Qu.:2017-05-13   3rd Qu.:2017-05-17                        
##  Max.   :2017-12-30   Max.   :2018-01-05                        
##                                                                 
##         Segment      Product.ID                   Category   
##  Consumer   :5191   Length:8090        Furniture      :1113  
##  Corporate  :1820   Class :character   Office Supplies:6026  
##  Home Office:1079   Mode  :character   Technology     : 951  
##                                                              
##                                                              
##                                                              
##                                                              
##       Sub.Category                   Product.Name      Sales          
##  Binders    :1523   Staple envelope        :  48   Min.   :    0.444  
##  Paper      :1370   Easy-staple paper      :  46   1st Qu.:   14.700  
##  Storage    : 846   Staples                :  46   Median :   41.370  
##  Art        : 796   Avery Non-Stick Binders:  20   Mean   :  187.453  
##  Furnishings: 494   Staples in misc. colors:  19   3rd Qu.:  154.900  
##  Appliances : 466   Staple remover         :  18   Max.   :13999.960  
##  (Other)    :2595   (Other)                :7893                      
##     Quantity         Discount          Profit         
##  Min.   : 1.000   Min.   :0.0000   Min.   :-6599.978  
##  1st Qu.: 2.000   1st Qu.:0.0000   1st Qu.:    1.814  
##  Median : 3.000   Median :0.1500   Median :    7.796  
##  Mean   : 3.791   Mean   :0.1573   Mean   :   24.756  
##  3rd Qu.: 5.000   3rd Qu.:0.2000   3rd Qu.:   24.998  
##  Max.   :14.000   Max.   :0.8000   Max.   : 6719.981  
## 

Dive Deeper:

Berapakah jumlah transaksi yang terjadi pada Order.Date tahun 2016 dari data retail_clean?

#cara 1
retail_2016<-retail_clean[retail_clean$Order.Date>="2016-01-01" & retail_clean$Order.Date<"2017-01-01",]

retail_2016

#cara 2
retail_clean[format(retail_clean$Order.Date, format="%Y")==2016,]

nrow() dan ncol()

nrow(retail_clean) # untuk mengekstrak jumlah rows

## [1] 9994

ncol(retail_clean) # untuk mengekstrak jumlah kolom

## [1] 12

Mengurutkan data berdasarkan profit terbesar ke terkecil, dan sebaliknya

# mengurutkan dari besar ke kecil
#cara 1
retail_clean[order(retail_clean$Profit, decreasing = TRUE),]

#cara 2
retail_clean[order(-retail_clean$Profit),]

# mengurutkan dari kecil ke besar
#cara 1
retail_clean[order(retail_clean$Profit, decreasing = FALSE),]

#cara 2
retail_clean[order(retail_clean$Profit),]

Another Subsetting Operator

Contoh penggunaan %in% untuk mensubset anggota pada variabel tertentu

bulan_genap <- c("February", "April", "June", "August", "October", "December")
bulan_genap

## [1] "February" "April"    "June"     "August"   "October"  "December"

#step
#1. derived kolom bulan dari order date
retail_clean

retail_clean$bulan <- months(x=retail_clean$Order.Date, abbreviate = F)
retail_clean

#2. subset data retail_clean untuk bulan genap
retail_clean[retail_clean$bulan %in% bulan_genap,]

#3. subset data retail_clean untuk bulan ganjil
retail_clean[!retail_clean$bulan %in% bulan_genap,]

Contoh penggunaan operator ! bukan

retail_clean[!retail_clean$bulan %in% bulan_genap,]

Contoh penggunaan operator != tidak sama dengan

retail_clean[!retail_clean$bulan != "Desember",]

Frequency Table table() dan proportion table prop.table()

Cek jumlah proporsi dari Ship.Mode, Segment, dan Category

Melihat jumlah tiap-tiap kategori menggunakan table()

seperti fungsi count() pada SQL

# untuk ship.mode
table(retail_clean$Ship.Mode)

## 
##    First Class       Same Day   Second Class Standard Class 
##           1538            543           1945           5968

# untuk segment
table(retail_clean$Segment)

## 
##    Consumer   Corporate Home Office 
##        5191        3020        1783

# untuk category
table(retail_clean$Category)

## 
##       Furniture Office Supplies      Technology 
##            2121            6026            1847

Cek jumlah proportion table dari variabel Ship.Mode dengan prop.table()

prop.table(table(retail_clean$Ship.Mode))

## 
##    First Class       Same Day   Second Class Standard Class 
##      0.1538923      0.0543326      0.1946168      0.5971583

Multiple Frequency Table dari Ship.Mode dan Category

table(retail_clean$Ship.Mode, retail_clean$Category)

##                 
##                  Furniture Office Supplies Technology
##   First Class          327             910        301
##   Same Day             119             326         98
##   Second Class         427            1152        366
##   Standard Class      1248            3638       1082

prop.table(table(retail_clean$Ship.Mode, retail_clean$Category), 
           margin = 1)

##                 
##                  Furniture Office Supplies Technology
##   First Class    0.2126138       0.5916775  0.1957087
##   Same Day       0.2191529       0.6003683  0.1804788
##   Second Class   0.2195373       0.5922879  0.1881748
##   Standard Class 0.2091153       0.6095845  0.1813003

prop.table(table(retail_clean$Ship.Mode, retail_clean$Category), 
           margin = 2)

##                 
##                   Furniture Office Supplies Technology
##   First Class    0.15417256      0.15101228 0.16296697
##   Same Day       0.05610561      0.05409890 0.05305901
##   Second Class   0.20132013      0.19117159 0.19815918
##   Standard Class 0.58840170      0.60371723 0.58581483

Kalau mau diconvert ke data frame

as.data.frame(prop.table(table(retail_clean$Ship.Mode, retail_clean$Category), 
           margin = 2))

Note: argumen margin = 1 untuk menjumlahkan perbaris, kalau 2 perkolom margin= yang mau di jadiin acuan 100% baris/kolom 1: baris 2: kolom tanpa margin: seluruh dataset

Dari tampilan di atas, apa yang dapat kamu simpulkan? 1. Ship.Mode Standart Class paling banyak digunakan di setiap kategori 2. Office Supplies paling banyak dibeli di seluruh mode shipment

Membuat Variabel baru bernama profitable yang dihasilkan dari jika profit > 0 maka profitable, jika profit <= 0 maka non-profitable

retail_clean$Profitable <- ifelse(test = retail_clean$Profit >0, yes = "Profitable", no = "Non Profitable")

Cek kembali data retail setelah penambahan variabel

retail_clean

Dive Deeper

1. Which following segment makes up the most of our “>1000 Sales” transaction? Subset the data for retail$Sales >= 1000 and then use table() with the “Segment” and “Category” variables as its parameters

2. Among the transactions that ship on “First Class” in retail_clean, how many of them were office supplies (to two decimal points)?

Pembahasan No. 1

retail_1000 <- retail_clean[retail_clean$Sales >= 1000,]
table(retail_1000$Segment, retail_1000$Category)

##              
##               Furniture Office Supplies Technology
##   Consumer           82              55         88
##   Corporate          58              45         51
##   Home Office        29              22         38

Pembahasan No. 2

retail_2 <- retail_clean[retail_clean$Ship.Mode=="First Class",]
round(prop.table(table(retail_2$Category)),2)

## 
##       Furniture Office Supplies      Technology 
##            0.21            0.59            0.20

Sample

RNGkind(sample.kind = "Rounding")

## Warning in RNGkind(sample.kind = "Rounding"): non-uniform 'Rounding' sampler
## used

set.seed(10) 
sample(1:13, 1)

## [1] 7

Cross Tabulation

menggunakan xtabs()

xtabs(formula = Profit ~ Ship.Mode+ Category, data= retail_clean)

##                 Category
## Ship.Mode         Furniture Office Supplies Technology
##   First Class     3066.9474      18400.3291 27502.5634
##   Same Day         797.3484       6423.5192  8670.8913
##   Second Class    4226.2614      27068.1676 26152.2064
##   Standard Class 10360.7156      70598.7849 83129.2870

as.data.frame(xtabs(formula = Profit ~ Ship.Mode+ Category, data= retail_clean))

menggunakan plot(xtabs()) untuk melihat visualisasi dari cross tab

plot(xtabs(formula = Profit ~ Ship.Mode+ Category, data= retail_clean))

menggunakan aggregate()

retail_agg<-aggregate(Profit ~ Ship.Mode+ Category, data = retail_clean, FUN = length)

retail_agg[order(retail_agg$Profit, decreasing = TRUE),]

pilihan FUN: mean, median, sum, min, max, var, sd, etc

Membuat Fungsi

pembagian <- function(x, y){
  m <- x / y
  m
}
pembagian(x = 30, y = 5)

## [1] 6

penjumlahan<-function(val1, val2){
  add<-val1+val2
  add
}

penjumlahan(val1=1,val2=9)

## [1] 10

cara install dan load library/package

EXTRA EXERCISE

A 30-minutes exercise in Break Out Room.

Create a new project, name it with cofee_study. Please create a Rmd, .RProj, and copy the coffee_study.txt dataset to the folder coffee_study. Then, answers these questions below:

1. Calculate the mean age for each combined level of both gender and drink

2. Calculate the mean age for each gender

3. How many male who drinks green tea?

4. Please order the dataset based on its score (Urutkan dari besar ke kecil)

5. How many people who drinks coffee who has lower than the median age?

After that, knit the .Rmd file to .html and collect the .html file to the classroom task named P4DS Exercise.

Link tambahan

Untuk mencari data yang bisa diakses secara gratis :

• kaggle
• UCI Machine Learning
• Tidy Tuesday

Untuk mempelajari lebih lanjut terkait data tanggal menggunakan as.Date

• Date Formats in R
• foragoodstrftime

Cara untuk membuat .RProj

Tahapan membuat .RProj ketika belum mempunyai folder untuk .RProj

• Klik menu File
• New Project
• New Directory
• New Project
• Isi directory name
• Specify working directory path yang diinginkan

Tahapan membuat .RProj ketika sudah mempunyai folder untuk .RProj

• Klik menu File
• New Project
• Existing Directory
• Tentukan RProj ingin diletakkan di folder apa (Specify Working Directory)

Beberapa package yang bisa diinstall untuk template R-Markdown

• install.packages(“prettydoc”)
• install.packages(“rmdformats”)
• install.packages(“hrbrthemes”)

Custom tampilan .Rmd bisa dipelajari lebih lanjut di :

• Help - Cheatsheets - RMarkdown Cheatsheet
• https://bookdown.org/yihui/rmarkdown/html-document.html
• Masuk ke rpubs.com untuk referensi laporan yang telah orang lain buat